Deciphering Cancer Cell Behavior From Motility and Shape Features: Peer Prediction and Dynamic Selection to Support Cancer Diagnosis and Therapy

Deciphering Cancer Cell Behavior From Motility and Shape Features: Peer Prediction and Dynamic Selection to Support Cancer Diagnosis and Therapy

Cell motility varies according to intrinsic features and microenvironmental stimuli, being a signature of underlying biological phenomena. The heterogeneity in cell response, due to multilevel cell diversity especially relevant in cancer, poses a challenge in identifying the biological scenario from...

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Bibliographic Details
Main Authors: Michele D'Orazio, Francesca Corsi, Arianna Mencattini, Davide Di Giuseppe, Maria Colomba Comes, Paola Casti, Joanna Filippi, Corrado Di Natale, Lina Ghibelli, Eugenio Martinelli
Format: Article
Language:English
Published: Frontiers Media S.A. 2020-10-01
Series:Frontiers in Oncology
Subjects:
machine learning
cell motility
peer prediction
dynamic feature selection
cancer heterogeneity
metastatic cancer cell detection
Online Access:https://www.frontiersin.org/articles/10.3389/fonc.2020.580698/full

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https://www.frontiersin.org/articles/10.3389/fonc.2020.580698/full

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